OSCR

Latent pain class identification, longitudinal transitions, and machine learning prediction of incident low back pain in middle-aged and older Chinese adults.

Overview

Authors: Junpeng Liu1, Zhiheng Zhao1, Shuhuan Li1, Xinglin Liu1, Sheyang Xu1, Bowen Lu1, Xianglong Meng1
  1. Department of Orthopaedic Surgery, Beijing Anzhen Hospital, Capital Medical University,Beijing101118, China
Institutions: Capital Medical University (China)
Journal: BMC medical informatics and decision making, volume 26, issue 1, article 177
Dates: received 9 December 2025; accepted 23 March 2026; published online 6 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s12911-026-03460-x · PMID 41943141 · PMCID PMC13188491 · OpenAlex W7151037104
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), pain (population)
Methods: Connectivity, Statistics, Machine learning, Preprocessing
Keywords: Latent class analysis, Latent transition analysis, Low back pain, Machine learning, Nomogram
MeSH: Low Back Pain*, Machine Learning*, Aged, Boosting Machine Learning Algorithms, China, Classification Algorithms, East Asian People, Female, Humans, Latent Class Analysis, Longitudinal Studies, Male, Middle Aged, Predictive Learning Models (* major topic)
Topic: Musculoskeletal pain and rehabilitation (Pharmacology, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (62276173); R&D Program of Beijing Municipal Education Commission (KZ202210025034); Chinese Institutes for Medical Research, Beijing (CX24PY15)
Citations: not cited yet (Europe PMC); 55 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

vizhub.healthdata.org/gbd-results

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 0 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1186/s12911-026-03460-x.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 14 MeSH terms, 3 funders, 53 references.

Cite

This paper

Liu, J., Zhao, Z., Li, S., Liu, X., Xu, S., Lu, B., & Meng, X. (2026). Latent pain class identification, longitudinal transitions, and machine learning prediction of incident low back pain in middle-aged and older Chinese adults. BMC medical informatics and decision making, 26(1), 177. https://doi.org/10.1186/s12911-026-03460-x

BibTeX

@article{liu2026latent,
author = {Liu, Junpeng and Zhao, Zhiheng and Li, Shuhuan and Liu, Xinglin and Xu, Sheyang and Lu, Bowen and Meng, Xianglong},
title = {{Latent pain class identification, longitudinal transitions, and machine learning prediction of incident low back pain in middle-aged and older Chinese adults}},
journal = {BMC medical informatics and decision making},
year = {2026},
month = apr,
volume = {26},
number = {1},
pages = {177},
publisher = {BMC},
issn = {1472-6947},
doi = {10.1186/s12911-026-03460-x},
url = {https://doi.org/10.1186/s12911-026-03460-x},
pmid = {41943141},
pmcid = {PMC13188491}
}

RIS

TY - JOUR
AU - Liu, Junpeng
AU - Zhao, Zhiheng
AU - Li, Shuhuan
AU - Liu, Xinglin
AU - Xu, Sheyang
AU - Lu, Bowen
AU - Meng, Xianglong
TI - Latent pain class identification, longitudinal transitions, and machine learning prediction of incident low back pain in middle-aged and older Chinese adults
T2 - BMC medical informatics and decision making
J2 - BMC Med Inform Decis Mak
PY - 2026
DA - 2026/04/06
VL - 26
IS - 1
SP - 177
SN - 1472-6947
PB - BMC
DO - 10.1186/s12911-026-03460-x
UR - https://doi.org/10.1186/s12911-026-03460-x
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s12911-026-03460-x",
"type": "article-journal",
"title": "Latent pain class identification, longitudinal transitions, and machine learning prediction of incident low back pain in middle-aged and older Chinese adults",
"container-title": "BMC medical informatics and decision making",
"author": [
{
"family": "Liu",
"given": "Junpeng"
},
{
"family": "Zhao",
"given": "Zhiheng"
},
{
"family": "Li",
"given": "Shuhuan"
},
{
"family": "Liu",
"given": "Xinglin"
},
{
"family": "Xu",
"given": "Sheyang"
},
{
"family": "Lu",
"given": "Bowen"
},
{
"family": "Meng",
"given": "Xianglong"
}
],
"container-title-short": "BMC Med Inform Decis Mak",
"volume": "26",
"issue": "1",
"page": "177",
"DOI": "10.1186/s12911-026-03460-x",
"PMID": "41943141",
"PMCID": "PMC13188491",
"ISSN": "1472-6947",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s12911-026-03460-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
6
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1002/jsp2.70200 [code]
A Porcine Model of Intervertebral Disc Injury Recapitulates Human Discogenic Pain Via Notochordal Cell Loss and Pain-Inducing Nucleus Pulposus Cell Emergence.
Journal: JOR spine
In common: pain, 2 references
[2] doi:10.1016/j.isci.2026.116443
High-definition transcranial direct current stimulation enhances exercise-induced hypoalgesia in patients with chronic low back pain.
Journal: iScience
In common: pain, 1 reference
[3] doi:10.3390/ijms27135711
Role of Supraspinal Neuroinflammation in Chronic Pain After Experimental Spinal Cord Injury-A Systematic Review.
Journal: International journal of molecular sciences
In common: pain, 1 reference
[4] doi:10.1186/s10194-026-02419-7 [code]
Transcriptional correlates of structure-function coupling plasticity in trigeminal neuralgia: unveiling the synaptic and metabolic associations.
Journal: The journal of headache and pain
In common: pain, 1 reference
[5] doi:10.1093/braincomms/fcag121 [code]
Anterior insular co-activation patterns associated with stress markers in chronic primary pain.
Journal: Brain communications
In common: pain, 1 reference
[6] doi:10.1371/journal.pone.0337726 [code]
Classification of chronic pain and spinal cord stimulation response using machine learning in magnetoencephalography data
Journal: n/a
In common: pain, 1 reference
[7] doi:10.1016/j.isci.2026.116085
Temporal multi-omic exploration of the ventral tegmental area in chronic pain and passive coping behaviors.
Journal: iScience
In common: pain, 1 reference
[8] doi:10.3390/genes17070813
Multilayer Genomic Characterization of a Shared Genetic Factor Linking Depression-Related Liability and Reduced Physical Function.
Journal: Genes
In common: 1 reference
[9] doi:10.1093/braincomms/fcag162 [code]
Disability profiles in progressive multiple sclerosis reflect pathology distribution, independent of clinical phenotype.
Journal: Brain communications
In common: 1 reference
[10] doi:10.1038/s41592-026-03194-8 [code]
Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.
Journal: Nature methods
In common: 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.